As shown in Fig. REF , researchers usually consider two kinds of transposed convolution operations: one adds padding around the input matrix and then applies the convolution operation, the other adds padding between the values of the input matrix followed by the direct convolution operation. The latter is also called fractionally strided convolution, since it works like doing convolution with a stride less than one. In the transposed convolutional layer, the upsampling level is controlled by the size of padding and it is essentially the opposite of the operation of the normal convolutional layer. Transposed convolutional layer is first proposed in FSRCNN [1]} and widely used in DL-based SISR models.
